RecHarness：面向自进化推荐系统的Bandit路由智能体框架

摘要

优化现代推荐模型仍然在很大程度上依赖工程师手动迭代调整网络结构、目标函数和训练策略。尽管基于大语言模型（LLM）的智能体能够自动化这一试错过程，但让LLM同时负责选择修改方向和生成具体假设，往往在有限的实验预算下导致搜索过程不稳定。受上述挑战启发，我们提出RecHarness——一种基于Bandit路由的智能体框架，用于推荐模型的自动优化。RecHarness将优化过程拆分为两个步骤：bandit路由器根据历史验证反馈选择下一个修改方向，LLM则在所选方向内生成具体的优化假设和可执行的代码修改。为了维持长期探索，RecHarness引入了一种跳跃盆地（jump-basin）机制：当局部编辑陷入停滞时，激活结构跳跃臂（structural-jump arm）。在多个推荐任务、数据集和模型骨干上，RecHarness相比基于LLM推理的搜索方法，取得了更稳定的性能提升，并能更有效地利用有限的实验预算。在一个大规模短视频广告平台上进行的为期7天的在线A/B测试中，所选候选模型使ADVV提升2.084%、Revenue提升0.534%、Exposure提升0.559%。代码已开源：https://github.com/6lyc/RecHarness。

English

Optimizing modern recommender models still depends heavily on engineers manually iterating over architectural, objective, and training-strategy changes. While LLM-based agents can automate this trial-and-error process, allowing the LLM to both select modification directions and generate concrete hypotheses often leads to unstable search under limited experiment budgets. Inspired by the above challenge, we propose RecHarness, a Bandit-Routed Agentic Harness for automated recommender model optimization. RecHarness separates the optimization process into two steps: a bandit router selects the next modification direction according to historical validation feedback, while the LLM generates a concrete optimization hypothesis and executable code edit within the selected direction. To sustain long-horizon exploration, RecHarness uses a jump-basin mechanism to activate a structural-jump arm when local edits stagnate. Across multiple recommendation tasks, datasets, and model backbones, RecHarness achieves more stable performance improvements and uses limited trial budgets more effectively than LLM-reasoning search. During a 7-day online A/B test on a large-scale short-video advertising platform, the selected candidate improves ADVV by 2.084%, Revenue by 0.534%, and Exposure by 0.559%. Code is available at https://github.com/6lyc/RecHarness.